Information regarding parking lot infrastructure may be used for various commercial and non-commercial applications. The ability for businesses and governments to identify and monitor parking lot infrastructure can play a critical role in determining economic development, predicting future trends, and in developing mapping software. Readily available remotely-sensed imagery has significantly improved this ability to identify and monitor parking lot infrastructure. However, such imagery includes vast amounts of data that is difficult to process efficiently to a high degree of accuracy. Prior art methods have been developed for detecting and identifying parking lots in remotely-sensed imagery with efficiency and accuracy drawbacks. Such methods are typically limited to specific types of imagery and are unreliable. They often locate parking lots where none exist or fail to identify parking lots that are remotely located, relatively small, or located on building roof-tops. Moreover, such methods typically lack sufficient detail related to the parking lot features, such as, for example, dimensions and locations of parking rows. Many prior art methods also rely heavily on manual extraction.